Python cultivation 7----iterator

Iterators

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Charging Tip

1.yield---> custom functions as long as Y writes a point, it means that the function is already a generator.

Yield--->m=yield 5 131 can be assigned as an expression, and the value of M is 131,5 is the value returned by the def-defined Function.

Yield--->next () function must be executed after The. __next__ () method is called, until the return value Stopiteration stopped

2.send () and Next ()

Send () is basically the same as next (), but send (*) can enter the expression value of yield, and next () cannot pass a specific value, only pass none in

Friendly Reminder: When the first call, use the next () statement or send (None), you cannot use Send to send a value other than None, otherwise it will be wrong, because there is no yield statement to receive this value

3. Three-dimensional expression----

def F ()

Return "+" if n>0 else "-" understanding satisfies if return otherwise "-"

1 Sum (x * * 2 for X in xrange (4)) to achieve "a-z"  Square summation      

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1. Iterator protocol

An iterator protocol is an object that must provide a next method that either returns the next item in the iteration, or causes a Stopiteration exception to terminate the iteration (only backward cannot go forward)

2.python ' in for loop mechanism

1. Turn them into an iterative object by calling The. __iter__ () method First.

2. The For loop then calls the __next__ method to take the value

The 3.for loop captures the stopiteration exception to terminate the iteration

3. The generator

1. Generator Concept: Automatic implementation of the iterator protocol (other data types need to call their own built-in __iter__ method), so the generator is an iterative object

1.1 Ways to provide generators:

1. Generator Function--custom function, Use yield statement to return results, yield statement return one result at a time, in the middle of each result, suspend the state of the function so that the next time it left the place to continue execution

2. Generator expression--like A list derivation, but the generator returns an object that produces results on demand, rather than building a list of results at a time

2. Generator functions

def F ():

Yield------default is

Advantage: the generator uses the yield statement to return a Value. The yield statement suspends the state of the generator function, preserving enough information so that it can continue to execute from where it left off.

Advantage One: the benefit of the generator is a deferred calculation that returns one result at a Time. That is, it does not produce all the results at once, which is useful for large data processing.

Advantage Two: Generator can also effectively improve code readability

Note: the generator can only be traversed once (hen life can only be a certain number of eggs, the end of the Dead)

　　

　　　　

Python cultivation 7----iterator